Synopsis#

The Nearest Set Replacement (NSR) method. A multi-target version of PS: The nearest sets are used to replace outliers, rather than subsets (as in PS).

BibTeX#

@inproceedings{JesseRead2008,
   author = {Jesse Read, Bernhard Pfahringer, Geoff Holmes},
   booktitle = {ICDM'08: International Conference on Data Mining (ICDM 2008). Pisa, Italy.},
   title = {Multi-label Classification Using Ensembles of Pruned Sets},
   year = {2008}
}

Options#

• -P <value>

  Sets the pruning value, defining an infrequent labelset as one which occurs <= P times in the data (P = 0 defaults to LC). default: 0 (LC)

• -N <value>

  Sets the (maximum) number of frequent labelsets to subsample from the infrequent labelsets. default: 0 (none) n N = n -n N = n, or 0 if LCard(D) >= 2 n-m N = random(n,m)

• -S <value>

  The seed value for randomization default: 0

• -W <classifier name>

  Full name of base classifier. (default: weka.classifiers.trees.J48)

• -output-debug-info

  If set, classifier is run in debug mode and may output additional info to the console

• -do-not-check-capabilities

  If set, classifier capabilities are not checked before classifier is built (use with caution).

• -num-decimal-places

  The number of decimal places for the output of numbers in the model (default 2).

• -batch-size

  The desired batch size for batch prediction (default 100).

Options specific to classifier weka.classifiers.trees.J48:

• -U

  Use unpruned tree.

• -O

  Do not collapse tree.

• -C <pruning confidence>

  Set confidence threshold for pruning. (default 0.25)

• -M <minimum number of instances>

  Set minimum number of instances per leaf. (default 2)

• -R

  Use reduced error pruning.

• -N <number of folds>

  Set number of folds for reduced error pruning. One fold is used as pruning set. (default 3)

• -B

  Use binary splits only.

• -S

  Do not perform subtree raising.

• -L

  Do not clean up after the tree has been built.

• -A

  Laplace smoothing for predicted probabilities.

• -J

  Do not use MDL correction for info gain on numeric attributes.

• -Q <seed>

  Seed for random data shuffling (default 1).

• -doNotMakeSplitPointActualValue

  Do not make split point actual value.

• -output-debug-info

  If set, classifier is run in debug mode and may output additional info to the console

• -do-not-check-capabilities

  If set, classifier capabilities are not checked before classifier is built (use with caution).

• -num-decimal-places

  The number of decimal places for the output of numbers in the model (default 2).

• -batch-size

  The desired batch size for batch prediction (default 100).